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Llama 2 is an open-source large language model (LLM) developed by Meta. It is a competent open-source large language model, arguably better than some closed models like GPT-3.5 and PaLM 2. It consists of three pre-trained and fine-tuned generative text model sizes, including the 7 billion, 13 billion, and 70 billion parameter models.

You will explore Llama 2s conversational capabilities by building a chatbot using Streamlit and Llama 2.

How different is Llama 2 from its predecessor large language model, Llama 1?

Llama 2 significantly outperforms its predecessor in all respects. These characteristics make it a potent tool for many applications, such as chatbots, virtual assistants, and natural language comprehension.

To start building your application, you have to set up a development environment. This is to isolate your project from the existing projects on your machine.

First, start by creating a virtual environment using the Pipenv library as follows:

Next, install the necessary libraries to build the chatbot.

Streamlit: It is an open-source web app framework that renders machine learning and data science applications quickly.

Replicate: It is a cloud platform that provides access to large open-source machine-learning models for deployment.

To get a Replicate token key, you must first register an account on Replicate using your GitHub account.

Once you have accessed the dashboard, navigate to the Explore button and search for Llama 2 chat to see the llama-270b-chat model.

Click on the llama-270b-chat model to view the Llama 2 API endpoints. Click the API button on the llama-270b-chat models navigation bar. On the right side of the page, click on the Python button. This will provide you with access to the API token for Python Applications.

Copy the REPLICATE_API_TOKEN and store it safe for future use.

First, create a Python file called llama_chatbot.py and an env file (.env). You will write your code in llama_chatbot.py and store your secret keys and API tokens in the .env file.

On the llama_chatbot.py file, import the libraries as follows.

Next, set the global variables of the llama-270b-chat model.

On the .env file, add the Replicate token and model endpoints in the following format:

Paste your Replicate token and save the .env file.

Create a pre-prompt to start the Llama 2 model depending on what task you want it to do. In this case, you want the model to act as an assistant.

Set up the page configuration for your chatbot as follows:

Write a function that initializes and sets up session state variables.

The function sets the essential variables like chat_dialogue, pre_prompt, llm, top_p, max_seq_len, and temperature in the session state. It also handles the selection of the Llama 2 model based on the user's choice.

Write a function to render the sidebar content of the Streamlit app.

The function displays the header and the setting variables of the Llama 2 chatbot for adjustments.

Write the function that renders the chat history in the main content area of the Streamlit app.

The function iterates through the chat_dialogue saved in the session state, displaying each message with the corresponding role (user or assistant).

Handle the user's input using the function below.

This function presents the user with an input field where they can enter their messages and questions. The message is added to the chat_dialogue in the session state with the user role once the user submits the message.

Write a function that generates responses from the Llama 2 model and displays them in the chat area.

The function creates a conversation history string that includes both user and assistant messages before calling the debounce_replicate_run function to obtain the assistant's response. It continually modifies the response in the UI to give a real-time chat experience.

Write the main function responsible for rendering the entire Streamlit app.

It calls all the defined functions to set up the session state, render the sidebar, chat history, handle user input, and generate assistant responses in a logical order.

Write a function to invoke the render_app function and start the application when the script is executed.

Now your application should be ready for execution.

Create a utils.py file in your project directory and add the function below:

The function performs a debounce mechanism to prevent frequent and excessive API queries from a users input.

Next, import the debounce response function into your llama_chatbot.py file as follows:

Now run the application:

Expected output:

The output shows a conversation between the model and a human.

Some real-world examples of Llama 2 applications include:

With closed models like GPT-3.5 and GPT-4, it is pretty difficult for small players to build anything of substance using LLMs since accessing the GPT model API can be quite expensive.

Opening up advanced large language models like Llama 2 to the developer community is just the beginning of a new era of AI. It will lead to more creative and innovative implementation of the models in real-world applications, leading to an accelerated race toward achieving Artificial Super Intelligence (ASI).

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How to Build a Chatbot Using Streamlit and Llama 2 - MUO - MakeUseOf

Topics such as climate change, cultural politics, and teacher evaluation comments presented deep research dives this summer for several Ohio Northern University College of Arts & Sciences undergraduates and professors. Polar SURF (Summer Undergraduate Research Fellowships) is an innovative ONU program that offers summer research opportunities for students interested in in-depth academic exploration begun by professors and scaled to students capabilities. Resulting were seven projects, ranging in focus from the sciences to the humanities, that introduced students to formal research methods typically reserved for graduate school studies. According to Brad Wile, Ph.D., associate dean for faculty affairs and chemistry professor, Polar SURF provides a shorter summer experience for capable students and committed professors compared to externally funded experiences like National Science Foundation research experiences for undergraduates (REU) program. Funded with endowed College support, Polar SURF also opens the field to disciplines beyond science. This past summer featured projects in areas such as communications, political science, toxicology, art and ethics. Wile said SURF allows students to extrapolate from professors research ideas and existing work and run with those, with faculty guidance. In some cases, students are required to produce a formal research paper on their findings. Publication in professional journals is a possibility for some projects. Having the interested approaches that weve seen these students and faculty take over the summer has been great, Wile said. Three Summer Polar SURF 2023 projects are highlighted below. Automating Identification of Toxic Student Evaluations Koen Suzelis and Gabriel Mott worked with John Curiel, Ph.D., assistant professor of political science, to develop a solution to unhelpful toxic comments that students contribute to professor evaluations. While studies have shown the most negative comments, such as those that are racist, are typically not as prevalent as neutral or positive constructive input that instructors can use to improve their teaching, they still have an outsized impact. Mostly students with strong feelings tend to write comments, the three wrote in their papers abstract. Among the most recallable are toxic comments, comments that are unhelpful/hurtful in harassment, outrage, or personal attacks. These in turn demoralize professors while unduly influencing administrator hiring/firing decisions. They act as a potent poison pill for many faculty across universities. To date, cost constraints prevent universities from systematically identifying and quarantining toxic comments, they continued. Suzelis, Mott, and Curiel created an automated machine learning tool that rather effectively and affordably flags nonproductive toxic comments in student evaluations. They collected hundreds of evaluations from ONU, Ohio State University, and University of North Carolina at Chapel Hill, and divided them into three categories: outrage, personal attacks, and prejudicial and bigoted comments. The paper also addresses reframing evaluation questions. Their method, which incorporates artificial intelligence, seeks to consistently, efficiently, and affordably flag toxic comments and excise those that would unduly bias university administrators against faculty while at the same time allowing for comments with the potential to offer meaningful feedback to remain, they wrote. Resulting is a tool that any school or individual educator will be able to use, and one that potentially could have multiple uses for any organization wanting to aggregate and isolate other written content. Scrutinizing a Super Bowl Ad The He Gets Us ad campaign, which first ran during the January 2023 Super Bowl, resulted in an intriguing research project for Devin Gelbrand and Megan Wood, Ph.D., assistant professor of communication and culture. Launched in 2022 by Christian philanthropy foundation The Signatry, the $100 million marketing campaign intends to overcome ideological divides by encouraging people to find commonality with Jesus. The groups publicity approach describes Jesus as one of radical forgiveness, compassion, and love, and portrays him as an immigrant, a refugee, a feminist, and a radical activist, explain Gelband and Wood in their research paper. Yet, they note, skeptics point out that some heavyweight behind-the scenes donors and the campaigns parent foundation have strong ties to conservative political projects and far-right ideologies that appear at odds with the campaigns inclusive messaging. Gelband and Woods research explores the media and cultural politics of the He Gets Us campaign, going beyond the common "culture-war" frame to investigate how the campaign's use of the third way rhetoric illuminates a contextually-significant set of tensions within the relationship between evangelical Christianity and the right-wing of U.S. politics. Historical context and precedent undergird their hypothesis, which posits the campaign is an effort to solve American Christianitys growing image problem with business savvy by relativizing and obscuring political differences to draw popular support while its benefactors fund candidates and polities that re-entrench those political divides and further decimate the rights of Americans. The two use a methodological approach called articulation, drawn from cultural studies, which helps researchers explore the relationship between a cultural phenomenon like the ad campaign and the social, economic, and political context of its production and reception. Gelband's and Woods mutual interest in the study of popular culture helped them pinpoint a collaborative research project. We had a very lively conversation about this Super Bowl campaign, which had just aired at the time, Wood said. Devin plans to parlay this project into another unique research question he will take on for his senior capstone this semester. Climate Change and Politics Mott also conducted an interdisciplinary analysis of environmental rhetoric in State of the Union addresses. Forrest Clingerman, Ph.D., religion and philosophy professor and Honors Program director, suggested Mott undertake a SURF project and I was immediately interested, Mott said. I used basic numeric analysis, political theory, and linguistic theory to investigate why presidents say the things they do, especially in the context of theState of the Union, Mott explained. I've found that presidentstend to discuss the environment more overall over time, but their normative arguments are extremely varied. Overall, Democrats discussed the environment more with more noticeable tonal characteristics, he concluded. Mott, who is wrapping up his research, said the project has been enjoyable. The greatest difficulty I've felt is trying to find direction for my project over the summer months we weren't in person, but the faculty members were all very helpful and supportive, he said. I've worked with a lot of new methods (humanities)that were interesting and challenging to adapt to relative to my usual practices, which are much more logically analytical. Mott hopes to publish his work in an undergraduate journal. Polar SURF is a great opportunity to experiment with interdisciplinary questions in humanities research, said Clingerman. Working with faculty members Clingerman, Jonathan Spelman, associate professor of philosophy, and Emily Jay, BFA 10, adjunct art professor, there was a collaborative group that fostered Motts work and the work of three other undergraduates Margaret Kurtz, who conducted an analysis on local churchs views on climate change; Madeline Alexander, who studied the ethics of activism; and Aubrey Davis, who used locally-sourced materials such as clay to examine environmental sustainability using artistic expression. Such an approach presents students with a broader context and multiple perspectives with which to investigate and formulate responses. Polar SURFs flexibility also allows students to still work during the summer while having this opportunity to do something really unique, Clingerman said.

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ONU's Polar SURF undergraduate research projects expand into the ... - Northern News

Artificial intelligence (AI) is making headlines everywhere. Yet AI applications and implications for older adults, particularly older women, have not been adequately contemplated.

Its no longer a moonshot idea from a science fiction movie.AI is already part of our daily lives Apples Siri, Amazons Alexa, self-driving cars. And now ChatGPT, an AI chatbot that has human-like conversations, composes music, creates art and writes essays.It has disrupted the world as we know it. Pundits who are not easily impressed often describe these advancements as scary good.

Many leaders have asked for a pause on AI development until we gain a better understanding of its impact. This is a good idea but for reasons well beyond those often identified.

We need to ask: How can we ensure that AIs reach is considering the unique needs of different populations? For example, many countries are becoming super-aged societies where women make up the majority of the older population. Is AI taking the needs of older adults into account?

Without thinking through these questions, we may leave older adults, particularly women, and other marginalized populations, open to discriminatory outcomes.

The needs of older women are often invisible to decision-makers. Older women are a unique population and often gendered ageism discrimination based on their age and sex causes their needs to be neglected. Research has already demonstrated that older women are more likely to experience adverse health outcomes and facepovertyand discrimination based on age and sex.

AI perpetuates this discrimination in the virtual world by replicating discriminatory practices in the real world. Whats worse is that AI automates this discrimination speeds it up and makes the impact more widely felt.

AI models use historical data. In healthcare, large data sets composed of personal and biomedical information are currently being used to train AI, but these data have, in many cases,excluded older adultsand women, making technologies exclusionary by design.

For example, AI has a valuable use in drug research and development, which uses massive data sets or big data. But AI is only as good as the data it gets and much of the world has not collected drug data properly. In the United States,until the 1990s, women and minorities were not required to be included in National Institute of Health (NIH) funded studies. Andup until 2019, older adults were not required to be included in NIH funded studies leaving a gap in our understanding of the health needs of older women in particular.

Excluding older women from drug data collection has been specifically detrimental because they are more likely to have chronic conditions, conditions that may require drugs, and are more likely to experienceharmful side effectsfrom medications.

Also, AI powered systems are often designed based on ageist assumptions. Stereotypes such as older adults being technophobes result in their exclusion from participation in the design of advanced technologies.

For example, women make up majority of the residents in long-term care homes.A studyfound that biases held by technology developers towards older adults hindered the appropriate utilization of AI in long-term care.

There also needs to be further thought given to loss of autonomy and privacy, and the effects of limiting human companionship because of AI. Older women are more likely to experience loneliness, yet AI is already being used in the form of companion robots. Their impact on older womens wellbeing, especially loss of human contact, is not well studied.

This is how older women get left out from properly benefitting from advancements in technology.

The World Health Organizations (WHO) timelypolicy briefaddressesAgeism in Artificial Intelligence for Healthand outlines eight important considerations to ensure that AI technologies for health address ageism. These include participatory design of AI technology with older people and age-inclusive data.

We would add the need to consider the differences between women and men throughout.All levels of government also need to think about how AI is impacting our lives and get innovative with policy and legal frameworks to prevent systemic discrimination.

Ethical guidelines and the ongoing evaluation of AI systems can help prevent the perpetuation of gendered ageism and promote fair and equitable outcomes.

Its time we rethink our approach and reimagine our practices, so that everyone can participate and take advantage of what AI has to offer.

About the Authors: Surbhi Kalia is the Strategy Consultant andDr. Paula Rochon is a geriatrician and the founding directorof theWomens Age Labat Womens College Hospital.

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Why Artificial Intelligence Needs to Consider the Unique Needs of ... - Women's eNews

What is image-to-image translation?

Image-to-image translation is a generative artificial intelligence (AI) technique that translates a source image into a target image while preserving certain visual properties of the original image. This technology uses machine learning and deep learning techniques such as generative adversarial networks (GANs); conditional adversarial networks, or cGANs; and convolutional neural networks (CNNs) to learn complex mapping functions between input and output images.

Image-to-image translation allows images to be converted from one form to another while retaining essential features. The goal is to learn a mapping between the two domains and then generate realistic images in whatever style a designer chooses. This approach enables tasks such as style transfer, colorization and super-resolution, a technique that improves the resolution of an image.

The image-to-image technology encompasses a diverse set of applications in art, image engagement, data augmentation and computer vision, also known as machine vision. For instance, image-to-image translation allows photographers to change a daytime photo to a nighttime one, convert a satellite image into a map and enhance medical images to enable more accurate diagnoses.

Image processing systems using image-to-image translation require the following basic steps:

A critical aspect of image-to-image translation is ensuring the model generalizes well in response to previously unseen or unsupervised scenarios. Cycle consistency and unsupervised learning help to ensure that if an image is translated from one domain to another and then back, it returns to its original form. Deep learning architectures, such as U-Net and CNNs, are also commonly used because they can capture complex spatial relationships in images. In the training process, batch normalization and optimization algorithms are used to stabilize and expedite convergence.

The two main approaches to image-to-image translation are supervised and unsupervised learning.

Supervised methods rely on paired training data, where each input image has a corresponding target image. Using this approach, the generated image system learns the direct mapping that's required between the two domains. However, obtaining paired data can be challenging and time-consuming, especially when dealing with complex image transformation.

Unsupervised methods tackle the image-to-image translation problem without paired training examples. One prominent unsupervised approach is CycleGAN, which introduces the concept of cycle consistency. This involves two mappings: from the source domain to the target domain and vice versa. CycleGAN ensures the target domain is similar to the original source image.

Image-to-image translation and generative AI in general are touted for being cost-effective, but they're also criticized for lacking creativity. It's essential to research the various AI models that have been developed to handle image-to-image translation tasks, as each comes with its own unique benefits and drawbacks. Research groups such as Gartner also urge users and generative AI developers to look for trust and transparency when choosing and designing models.

Some of the most popular models include the following:

Image-to-image translation is a popular generative AI technology. Learn the eight biggest generative AI ethical concerns.

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What Is Image-to-Image Translation? | Definition from TechTarget - TechTarget

Dr. Tomas Chamorro-Premuzic and James Spiro

(Photo: Zoom/Sinay David)

On a philosophical or testimonial level, if you look at most of the mainstream science, neuroscience, behavioral science, there is probably 80% consensus that free will is actually overrated or overstated, said Dr. Tomas Chamorro-Premuzic, author of I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique. We think we are in control of the decisions we make, but actually there are so many serendipitous and biologically driven courses of our decision.

Dr. Tomas Chamorro-Premuzic is an organizational psychologist who works mostly in the areas of personality profiling, people analytics, talent identification, the interface between human and artificial intelligence, and leadership development. He is the Chief Innovation Officer at ManpowerGroup, a professor of business psychology at University College London and at Columbia University, co-founder of deepersignals.com, and an associate at Harvards Entrepreneurial Finance Lab.

He is the writer behind books such as Why Do So Many Incompetent Men Become Leaders?, The Future of Recruitment: Using the New Science of Talent Analytics to Get Your Hiring Right, and this years I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique. Joining CTech for its new BiblioTech video series, he discusses the integration of AI into our lives and how we can keep our unique creativity and value in an increasingly digital world.

Leaving aside these philosophical discussions what I highlight in the book is that if we get to a point where our decisions are so predictable that AI can make most of these decisions, even if we are not automated and replaced by AI, surely we need to question our sense of subjective free will?

Many of the topics that Chamorro-Premuzic addresses in the book relate to the impact that AI will have on our lives and how different generations might respond to the algorithms living beside us. For example, he cites tech leaders like Bill Gates and Elon Musk, who present concerning views of AI, but also respond positively to how Gen Z might learn to adopt such technologies.

One of the things that the digital age has introduced is ever more and more ADD-like behaviors, he continued. We are pressed to do things quicker and quicker. And therefore there are few rewards for pausing and thinking.

Even though he believes humans are perfectly capable of stopping and taking time to consider their thoughts and actions, most of the decisions today in the AI age are so fast that they become very predictable and therefore easily outsourced to machines.

Gen Z and the next generation will need to showcase their expertise in a different area or a different way, he told CTech. Expertise is mutating from knowing a lot of answers to asking the right questions - from memorizing and retrieving facts to knowing how, why, and where the facts are wrong Demonstrating and cultivating expertise is a big challenge for the young generations.

Tomas, in your book you tackle one of the biggest questions facing our species: "Will we use artificial intelligence to improve the way we work and live, or will we allow it to alienate us?" Why did you find that now was the moment that this question needed to be asked and why did your book come out when it did?

I wrote 4-5 years ago that AI could be a really powerful tool to translate data and make leadership selection more data-driven with my first book, Why Do So Many Incompetent Men Become Leaders? (And How to Fix it). Then came The Future of Recruitment: Using the New Science of Talent Analytics to Get Your Hiring Right, which was about practical advice on how organizations can do that. Then, I was already contracted to do a new book during the pandemic, and on a personal level I found myself interacting with AI so much and interacting with other humans so little, that I thought this thing was really about to take off especially if we will be in lockdown for a while.

I started to look at the wider impact of AI and human behavior. Coincidentally the book was due to launch when OpenAI released ChatGPT which I always say is good and bad. Its good because there is more interest now for a book that explores the implications for human intelligence and human creativity in an age where we can outsource much of our thinking to machines. And it's bad because I had to write it myself, I couldn't rely on ChatGPT to write it! I think the next one will probably be written by AI and I will edit it!

I'd like to highlight what some of the tech leaders of today have said about AI, which you address at the start of your book:

You comment that Bill Gates is concerned about super intelligence; Stephen Hawking noted that Super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we are in trouble. Finally, you highlight how Elon Musk labeled AI a fundamental risk to the existence of human civilization - although you point out it hasn't stopped him from trying to implant it into our brains.

Tomas, why are we pursuing such a scary and unknown technology?

We're pursuing it mostly for two reasons. First, over the past 10 years, we have amassed so much data that we dont have enough human resources or human intelligence available to analyze that data. Also, we had to rely on a large language model, or some version of AI, to help us make sense of the data and actually make decisions in a more efficient, quick, and effortless way which is needed in a work that is so complex.

The second reason is that human beings are very lazy. We love to optimize everything for familiarity, for predictability. You could either sit down to watch any movie that Netflix recommends to you and after five seconds youll be watching a movie, or you could do what I do which is dismiss the algorithm, dig deeper, and waste two hours of my life. By the time I actually find the movie I want to watch it is time to go to sleep. We are trading off efficiency, which means lazy, fast, and furious decision-making, for deep, thoughtful, and expert-like decisions.

It is the same whether we are choosing a job, a romantic partner, a restaurant, a hotel, or what we consume in terms of news. This is why AI has been introduced as a potential tool that can enhance our productivity. Even if we're not necessarily going to invest whatever savings we gain from the productivity that AI uses into more thoughtful, creative, and intellectually fulfilling activities. Therein lies the problem.

I want to address some of the more nefarious things you mention and some of the ways that AI is affecting us in ways we don't understand. We speak about AI in the world, but how much choice do we have and how much is just an illusion of choice?

On a philosophical or testimonial level, if you look at most of the mainstream science, neuroscience, behavioral science, there is probably around 80% consensus that free will is actually overrated or overstated. But it is mostly an illusion. We think we are in control of the decisions we make but actually, there are so many serendipitous and biologically driven courses of our decisions.

Leaving aside these philosophical discussions which are hard to verify and often don't mean much to the average consumer, it is clear to me: If we get to a point where our decisions are so predictable that AI can make most of these decisions, even if we are not automated and replaced by AI, surely we need to question our sense of subjective free will?

If when I'm writing an email to you and Googles auto-complete version is correct 95% of the time, then I have to wonder whether I really am an agentic creative human that still has some choice or whether it's more deterministic than we think. I think the way to think about these issues is that we are mostly free to choose, or at least we feel we are free to choose, but that doesn't necessarily mean we want to pause, think, and choose. One of the things that the digital age has introduced is even more and more ADD-like behaviors. We are pressed to do things quicker and quicker and therefore there are few rewards for pausing and thinking, which explains the rise of things like mindfulness movements, apps, and people who do digital detoxes.

We are perfectly capable of pausing and thinking, but most of the decisions we are making in the AI age are so fast that they become very predictable and therefore they can be outsourced to machines.

I'd like to elaborate on what you mention in the book which you call a "Crisis of Distractability". I think it really sums up where so many of us are today online. What did you mean by that and how is it manifesting itself in recent years?

Around 11 years ago I went to a digital marketing conference where you had all the big tech firms. For the first time, some people were introducing the notion of the second screen, which was very counterintuitive and bold at the time. People were watching TV and holding their iPads, or they were looking at their smartphones and now theres a second screen market.

Now, we all have 3-4 screens that we interact with all the time. Life itself has been downgraded to a distraction. You're almost distracted when you can't pay attention to your apps or your social media feeds. You get FOMO if you can't interact with people digitally and you have to pay attention to the analog world.

In terms of productivity, I think this is really important because even though we keep on arguing about whether technology and GenAI are going to lead to a productivity gain or the demise of human civilization, the tech firms keep telling us it will make us healthier, fitter, happier, and more productive.

Actually the productivity data is very clear. Our productivity went up between 2000-2008 in the first wave of the digital revolution, only to start to stagnate or stall after that, after the advent of social media. Roughly 60-75% of smartphone use occurs during working hours when they're working from home or in an office and 70% of workers report being distracted. In the U.S. alone, digital distractions cost the U.S. economy $650 billion dollars in productivity loss per year, which is 15 times more than the cost of absentees, turnover, and sickness. Multitasking, which we all do, results in a deficit of our intellectual cognitive performance of around 10 IQ points. It's basically as debilitating as smoking weed, presumably minus the benefits.

We think and fool ourselves into thinking that we can multitask, but every time you switch from one task to the other and you go back, youve lost the equivalent of 26 minutes of concentration on that task. Technology might improve productivity but sometimes you become more productive if you ignore or have the ability to resist technology as well.

There is a whole new generation in Gen Z who are growing up in the world youve been outlining - with AI and a search for uniqueness. What are some of the challenges they're going to have when trying to find their voice or establish their careers or relationships?

The main challenge will be to demonstrate social proof. If you just enter or start your career, no matter how smart you are, it is a very steep curve to demonstrate to others that you can provide more value than what you can get from AI. You're probably paying a lot of attention to ChatGPT and other forms of GenAI in terms of their ability to produce an article, or an opinion piece. Youre probably, in your area of expertise, able to spot the errors, but the reason you are adding value to that is because of your track record and experience, that actually you know your stuff.

If you're just starting, it's very difficult to persuade people that you have that expertise. Gen Z and the next generation will need to showcase their expertise in a different area or a different way. Expertise is mutating from knowing a lot of answers to asking the right questions - from memorizing and retrieving facts to knowing how, why, and where the facts are wrong. Fundamentally, to make decisions on the basis of information that might be correct or incorrect. Demonstrating and cultivating expertise is a big challenge for the young generations.

I heard that the future artists or engineers wont be coders, theyll be prompt engineers. Theyre going to know how to get the best out of the AI, which at the moment folks like me are walking around with our blindfolds not knowing what it's capable of.

There is an argument to be made that as soon as there's enough prompt engineers prompting AI, AI will learn to prompt itself then we will need to move to the next iteration. There is going to be a very intense cat-and-mouse race or game where as soon as we develop something it can be automated. And we have to develop something else and it can be automated.

Creativity is really critical. Spotify probably has enough data to automate 80% of its artists because it has an algorithm to understand what people like and most music can be pre-processed and done synthetically. Even if it automated 100% of its content, it probably wouldnt kill musicians. It would push artists to invent the next version of music. I think that's how we need to think about every form of performance that is intellectually fueled or creatively or artistically informed.

You touch on popular content in the book, such as Netflix's The Social Dilemma, the famous book Surveillance Capitalism, and of course Black Mirror, which is the modern-day Twilight Zone. What can readers learn from I, Human?

Hopefully they will learn a little bit about AI, especially if they don't have technical backgrounds on it. It's designed for people with no knowledge for people to understand what AI is and what it isnt - to understand how the algorithms that we interact with on a regular basis are reshaping our behavior.

Culture is always a big influence on how we behave. The average person today behaves differently from the average person in the Renaissance, medieval times, or in ancient Greece or Rome even though our hardware or DNA is the same. What I argue is that the current culture could be defined universally as the AI age, and with that comes certain behavioral traits and markers they will discover in their book.

The final part is a call to action, how we need to change if we want to ensure that the AI age is also the human AI age and that we use this technological invention to upgrade ourselves.It finishes on a relatively optimistic note with a call to action to rediscover some of the qualities that make us who we are. AI will probably not harm things like dep curiously, creativity, self-awareness, empathy, and EQ. The argument is that AI will probably win the IQ battle but the EQ battle could be won by humans.

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There is probably an 80% consensus that free will is actually ... - CTech